1. Field of the Invention
The present invention relates to a wireless communication system and, more particularly, to detecting the presence of a signal in a wireless communication system based on Cognitive Radio (CR) technology.
2. Discussion of the Related Art
The present invention can be applied to technologies pertinent to a communication system, such as a cellular system, a relay system, an adhoc network, and CR communication. In communication systems, research has long been carried out on a signal detection technique. The research is made in the direction in which a detection probability is maximized and a false alarm probability is minimized.
In Korea, wireless communication technology was very limitedly used up to the 1980s (i.e., the analog generation), but has been dramatically developed after the commercialization of CDMA mobile communication in the 1990s. Today, a ubiquitous information society is coming soon. In the ubiquitous information society, however, the shortage of the frequency is serious because the demand for frequency resources is much greater than the supply thereof. Accordingly, the value of frequency resources becomes more important in line with the development of wireless communication. The efficiency of an actually distributed and measured frequency is 30% or less on average. In order to solve the shortage of frequency resources, it is necessary to develop sharing technology for efficiently using unused frequency resources.
CR technology has recently been in the spotlight as technology for significantly increasing the efficiency of frequency resources which are inefficient or not used, from among distributed frequency resources. The CR technology was developed by combining software defined radio (SDR)-based wireless communication technology and cognition technology. The SDR technology is technology in which software can be downloaded into hardware capable of performing wideband signal processing over a wide frequency band and a variety of functions can be performed. The cognition technology is computer technology for performing self-learning, while continuously gathering surrounding information, and dealing with circumstances. CR communication is being applied to IEEE (Institute of Electrical and Electronics Engineers) 802.22 which is one of wideband wireless communication schemes on which standardization is in progress.
In the CR technology, a surrounding spectrum where a device is placed is sensed, and communication is performed using empty channel information. In the case in which an incumbent user uses a corresponding frequency, communication is performed using another frequency band without interfering with the incumbent user anytime. For this function, a CR apparatus must determine whether the incumbent user uses a specific frequency by periodically placing a quiet period during the time for which the corresponding frequency is used. If the incumbent user is detected, the CR apparatus must move to another channel within a predetermined time or stop the use of the corresponding channel. In the case in which, while a CR apparatus uses a specific frequency, an incumbent user attempts to use the specific frequency, the CR apparatus detects such an attempt through spectrum sensing, moves to another frequency, and performs communication.
The term ‘spectrum sensing’ refers to the detection of the presence of a frequency being used by detecting a frequency spectrum environment. The spectrum sensing technology is a signal detection technique for detecting the presence of a user' signal in a corresponding frequency in order not to interfere with an incumbent user who uses an authorized frequency band. The spectrum sensing technology is core frequency resource sharing technology.
To this end, there is a need for a signal presence detection technique for detecting the presence of a user' signal in a corresponding frequency. Research is being carried out on lots of techniques in order to increase detection performance. The techniques can include, for example, a matched filter detection method, an energy detection method, and a cyclostationary characteristic detection method. The matched filter detection method is excellent in the performance, but disadvantageous in that signal information received from a transmitter must be fully known for the excellent performance. The energy detection method is simple because of a low computational load, but disadvantageous in that it is poor in the performance and sensitive to noise variance estimation error. The cyclostationary characteristic detection method is insensitive to noise variance estimation error, but has a high computational load. Meanwhile, modulation and demodulation methods used in most communication systems, such as CDMA, OFDM, and SC-FDE, are included in a linear modulation method. There is a need for an optimal signal detection technique having a low computational load while detecting a linearly modulated signal having a wide cyclostationary.
From among the detection methods, there is a need for a method of, in particular, maximizing a probability that when a transmission signal exists, a receiver will determine that the transmission signal does exist (hereinafter referred to as a ‘detection probability’) and minimizing a probability that when a transmission signal does not exist, a receiver will determine that the transmission signal exists (hereinafter referred to as a ‘false alarm probability’). If the existing Neyman-Pearson optimum detector is used, the false alarm probability can be minimized when the detection probability is constant or the detection probability can be maximized when the false alarm probability is constant. In order to use the Neyman-Pearson optimum detector, there is a need for a process of finding an inverse matrix of a covariance matrix of signal component vectors or finding an eigenvalue and an eigenvector. In general, the process of finding the inverse matrix or the eigenvalue and the eigenvector requires a high computational load. Accordingly, there is a need for a method of detecting the presence of a signal, having the same or almost the same performance as the Neyman-Pearson optimum detector while requiring a low computational load.